{"id":"https://openalex.org/W4415593657","doi":"https://doi.org/10.1109/tits.2025.3621423","title":"Coordinated Ramp Metering Strategy Based on Deep Reinforcement Learning Incorporating Attention Mechanism","display_name":"Coordinated Ramp Metering Strategy Based on Deep Reinforcement Learning Incorporating Attention Mechanism","publication_year":2025,"publication_date":"2025-10-27","ids":{"openalex":"https://openalex.org/W4415593657","doi":"https://doi.org/10.1109/tits.2025.3621423"},"language":null,"primary_location":{"id":"doi:10.1109/tits.2025.3621423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3621423","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Shixuan Yu","orcid":"https://orcid.org/0009-0007-9442-3053"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shixuan Yu","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0007-9442-3053","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062152393","display_name":"Yu Han","orcid":"https://orcid.org/0000-0002-3655-3374"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Han","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-3655-3374","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.4725,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.65272941,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"27","issue":"2","first_page":"2794","last_page":"2806"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12546","display_name":"Smart Parking Systems Research","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12546","display_name":"Smart Parking Systems Research","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":0.9872000217437744,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14011","display_name":"Elevator Systems and Control","score":0.9628000259399414,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8101000189781189},{"id":"https://openalex.org/keywords/metering-mode","display_name":"Metering mode","score":0.741100013256073},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.6420000195503235},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.5920000076293945},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4332999885082245},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.42480000853538513},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.367900013923645}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8101000189781189},{"id":"https://openalex.org/C30905978","wikidata":"https://www.wikidata.org/wiki/Q815598","display_name":"Metering mode","level":2,"score":0.741100013256073},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6953999996185303},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.6420000195503235},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.5920000076293945},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4332999885082245},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.42480000853538513},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36550000309944153},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.35749998688697815},{"id":"https://openalex.org/C2778391309","wikidata":"https://www.wikidata.org/wiki/Q7832527","display_name":"Traffic simulation","level":3,"score":0.34940001368522644},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C79487989","wikidata":"https://www.wikidata.org/wiki/Q934680","display_name":"Vehicle dynamics","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.2888999879360199},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.25459998846054077},{"id":"https://openalex.org/C91575142","wikidata":"https://www.wikidata.org/wiki/Q1971426","display_name":"Optimal control","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3621423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3621423","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2136783178","display_name":null,"funder_award_id":"52525204","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3716380425","display_name":null,"funder_award_id":"52131203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5180636669","display_name":null,"funder_award_id":"52232012","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1967804417","https://openalex.org/W1989486490","https://openalex.org/W2014847204","https://openalex.org/W2017633777","https://openalex.org/W2018846421","https://openalex.org/W2026799006","https://openalex.org/W2036271427","https://openalex.org/W2065672848","https://openalex.org/W2085679358","https://openalex.org/W2094652234","https://openalex.org/W2101383794","https://openalex.org/W2119717200","https://openalex.org/W2139316239","https://openalex.org/W2140722533","https://openalex.org/W2172968643","https://openalex.org/W2583813242","https://openalex.org/W2903709398","https://openalex.org/W2983178256","https://openalex.org/W2989932935","https://openalex.org/W3003257820","https://openalex.org/W3200607907","https://openalex.org/W4206007985","https://openalex.org/W4212811589","https://openalex.org/W4307897070","https://openalex.org/W4308080010","https://openalex.org/W4388320480","https://openalex.org/W4390442413","https://openalex.org/W4390492434","https://openalex.org/W4400950475"],"related_works":[],"abstract_inverted_index":{"This":[0,97],"paper":[1],"presents":[2],"a":[3,33,112,132],"deep":[4],"reinforcement":[5],"learning":[6],"(DRL)-based":[7],"strategy":[8,70,142],"for":[9,21],"coordinated":[10],"ramp":[11,119],"metering.":[12],"Existing":[13],"DRL-based":[14,145],"strategies":[15],"often":[16],"fail":[17],"to":[18,53,83,102,111],"explicitly":[19],"account":[20],"the":[22,45,50,68,76,85,89,100,104,140],"correlation":[23],"between":[24,88],"on-ramp":[25],"flows":[26],"and":[27,94],"congestion":[28],"at":[29],"different":[30],"bottlenecks.":[31],"As":[32],"result,":[34],"RL":[35,77],"agents":[36],"must":[37],"infer":[38],"these":[39],"relationships":[40],"through":[41,127],"extensive":[42],"interactions":[43],"with":[44],"environment,":[46],"which":[47],"can":[48],"cause":[49],"control":[51],"policy":[52],"become":[54],"stuck":[55],"in":[56,116,147],"local":[57],"optima,":[58],"limiting":[59],"potential":[60],"traffic":[61,90,129,149],"performance":[62],"improvements.":[63],"To":[64],"address":[65],"this":[66],"problem,":[67],"proposed":[69,123,141],"integrates":[71],"an":[72],"attention":[73],"mechanism":[74,98],"into":[75],"agent\u2019s":[78],"state":[79],"function,":[80],"enabling":[81],"it":[82],"capture":[84],"spatial-temporal":[86],"correlations":[87],"states":[91],"of":[92,107],"on-ramps":[93],"mainstream":[95,113],"segments.":[96],"allows":[99],"agent":[101],"evaluate":[103],"relative":[105],"importance":[106],"each":[108],"on-ramp\u2019s":[109],"contribution":[110],"bottleneck,":[114],"resulting":[115],"more":[117],"effective":[118],"metering":[120],"actions.":[121],"The":[122],"method":[124],"is":[125],"validated":[126],"microscopic":[128],"simulation":[130],"on":[131],"real-world":[133],"road":[134],"network.":[135],"Experimental":[136],"results":[137],"show":[138],"that":[139],"outperforms":[143],"state-of-the-art":[144],"approaches":[146],"improving":[148],"performance.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-28T00:00:00"}
